The subject matter disclosed herein relates to the use of virtual flow metering in resource production contexts, such as oil and gas production.
In various contexts where a fluid medium, either liquid or gas, is flowed between various locations, the control of the flow may be controlled at least in part using measured flow aspects. Various types of flow meters may be provided to provide data on the flow of the fluid at a given time and at a given location. By way of example, in a hydrocarbon production context, flow meters may measure flow at one or more locations in the production path to provide data on the flow of the production fluid through various parts of the production system.
By way of example, two types of flow meter technologies are physical flow meters and virtual flow meters. In the context of physical multiphase flow meters, these flow meters typically estimate the flow rate of each phase in question by utilizing a combination of techniques, which may each in turn utilize various electronic sensing devices, such as microwave sensors, electrical impedance sensors, doppler ultrasound sensors, gamma ray sensors, and so forth.
There may be various drawbacks associated with the use of physical flow meters, including cost (since expensive sensors are typically employed), reliability (since complex sensors are typically more susceptible to failure), communication and power supply issues (e.g., high power consumption to keep sensors working demands specific umbilical pipes), and precision and accuracy (generally, physical flow meters present measurement errors due to the complexity of a multiphase flow).
Virtual flow meters may also utilize various sensor systems and algorithms for estimating flow rates. However, virtual flow meters typically make use of less complex types of sensors (e.g. temperature and pressure sensors) from whose measurements flow data is estimated. Both the physical and virtual flow metering approaches typically utilize complex data-fusion algorithms for estimating flow rates based on the measurements provided by the sensing units.
The maintenance of virtual flow meter accuracy over the life of a production site (e.g., an oil or gas field) is one challenge to the successful deployment of virtual flow meters at certain sites, such as subsea locations. The use of virtual flow meters may be subject to errors attributable primarily to two sources: models and sensor measurements. Model errors may be related either to mathematical modeling not adequately addressing the underlying physics or wrong (or varying) parameter assumptions (pipe roughness variation due to the incrustation of minerals, diameter variation due to the formation of wax, and so forth). Sensor measurements can be subjected to bias, drifts, precision degradation, or even total sensor failure. Thus, in order to maintain the estimation performance of the virtual flow meter, it must be periodically tuned to address these potential sources of error. However, assessment of such changes may be difficult in a non-controlled production site.
By way of example, the sensors in question may be prone to inaccuracies. If these inaccuracies are not explicitly taken into account in the tuning of a virtual flow meter, only mean values are available as sources of information for the calibration and estimation processes. Calibration improvement based solely on mean values can be misleading or erroneous, thus preventing proper tuning of the virtual flow meter.